vault backup: 2025-01-12 21:43:10

.obsidian/workspace.json

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Biometric Systems/notes/11. Fingerprints.md

@@ -113,7 +113,7 @@ The local orientation of the ridge line in the position [i, j] is defined as the
 ##### Frequency map
 the frequency of the local ridge line $f_{xy}$ at the point $[x, y]$ is defined as the number of ridges per unit length along a hypothetical segment centered at $[x, y]$ and orthogonal to the orientation of the local ridge.
 - by estimating the frequency in discrete locations arranged in a grid, we can compute a frequency image F:![[Pasted image 20241127225853.png]]
-- a possible approach is to count the averaage number of pixels between consecutive peaks of gray levels along the direction orthogonal to the local orientation of the ridge line
+- a possible approach is to count the average number of pixels between consecutive peaks of gray levels along the direction orthogonal to the local orientation of the ridge line
 
 ##### Singularities
 Most of approaches are based on directional map.
@@ -161,7 +161,7 @@ Hybrid method based on comparison of minutiae texture: combines the representati
 	- rotation parameter is the average of rotation of all the individual pairs of corresponding minutiae
 	- translation parameters are calculable using spatial coordinates of the pair of reference minutiae which produced the best alignment
 
-##### Masking and tesselation
+##### Masking and tessellation
 After masking the background, the images are normalized by building on them a grid that divides them into  series of non-overlapping windows of the same size. Each window is normalized with reference to a constant mean and variance. Optimal size for 300 DPI is 30x30, as 30 pixel is the average distance inter-solco.
 ![[Pasted image 20241128000431.png]]
 

Biometric Systems/notes/7. Face recognition 3D.md

@@ -96,7 +96,8 @@ si possono usare i landmark
 2. allineando facce minimizzando la distanza tra punti corrispondenti
 
 ##### Fine
-Algoritmo ICP
+Algoritmo ICP (Iterative Closest Point)
+https://www.youtube.com/watch?v=QWDM4cFdKrE
 Date due superfici 3D
 1. trova un iniziale match tra le due (mapping di punti/superfici/linee/curve)
 2. calcola la distanza tra le superfici con il metodo least squares